In the years following the Great Recession, the signals for a
recovery of the U.S. labor markets were mixed: while the unemployment
rate declined to historically low levels, labor force participation
rates also declined. This observation raised doubts on the ability of
the unemployment rate alone to accurately represent the state of
resource utilization in the labor market. (1) In Hornstein, Kudlyak, and
Lange (2014), we therefore proposed an indicator of resource utilization
in the labor market, a nonemployment index (NEI), that is more
comprehensive than the standard unemployment rate. In this article, we
relate our NEI to recent research on frictional unemployment in labor
markets and thereby provide a theoretical grounding for the NEI beyond
the heuristic justifications for its usefulness in our previous work.

More than 30 years ago, Flinn and Heckman (1983) pointed out that
the distinction between those being unemployed and those being out of
the labor force (OLF) is not clear cut but a matter of degree. For
example, the unemployed, that is, those nonemployed who are actively
searching for work, are twice as likely to make the transition to
employment within a month than those nonemployed who express a desire to
work but do not actively engage in job search activities, and they are
three times as likely to make the transition to employment than those
who do not even express a desire to work. Thus even though the
differences in employment transition probabilities are quantitatively
large, they do not suggest a qualitative difference between being
unemployed and being OLF. Furthermore, despite the substantially lower
employment transition probabilities for OLF, on average, every month
twice as many people make the transition from OLF to employment than do
from unemployment.

The Diamond-Mortensen-Pissarides search-matching framework
interprets new employment as being "produced" by matching job
seekers with open positions. (2) The standard approach assumes a
homogeneous search pool, that is, each searcher is equally likely to
make the transition to employment. Recent extensions have emphasized the
heterogeneous nature of the search pool, that is, the persistent
differences in search efficiency between unemployment and OLF, which is
reflected in persistent differences of employment transition
probabilities, for example, in Veracierto (2011), Diamond (2013), Elsby,
Hobijn, and Sahin (2015), Barnichon and Figura (2015), and Hornstein and
Kudlyak (2016). Most of this work is done in the context of estimating
matching efficiency in the labor market, that is, the extent of labor
market frictions. Accounting for heterogeneity in the search pool leads
to smaller estimated declines in matching efficiency, in part since
heterogeneity introduces systematic positive comovement between total
nonemployment and the average search efficiency of the heterogeneous
search pool. Within this generalized matching framework, we interpret
our proposed NEI as the quality-adjusted measure of the search pool.

This article is structured as follows. We first review the
search-matching framework and how it accounts for changes in average
employment transition rates with homogeneous and heterogeneous search
pools. We then characterize the pool of nonemployed in the Current
Population Survey (CPS) in terms of their average transition rates to
employment. Finally, we construct a sequence of NEIs with increasing
coverage of the nonemployed, the most comprehensive of them being the
NEI proposed in Hornstein et al. (2014). We show how these NEIs fit into
a generalized search-matching framework with heterogeneous search pools
and study their implications for measured changes in matching
efficiency. We should note that there is substantial overlap between
this paper and Hornstein et al. (2014), especially as it relates to the
characterization of the nonemployed in the CPS.

1. GENERALIZED MATCHING FUNCTIONS

The aggregate search and matching function in macro-labor models
describes the "production" of hires as a function of the
stocks of job seekers and vacancies and an exogenous shift term denoting
the aggregate efficiency of the matching process. The standard approach
for the search and matching function assumes that the inputs are
homogeneous. We augment the standard search and matching function by
allowing for fixed heterogeneity across observed groups of job seekers.

The Matching Function with Homogeneous Search

Consider an economy where unemployed workers need to be matched
with open positions. Assume that all workers and open positions are
homogeneous, but that for some reason the assignment of unemployed
workers to open positions is a time-consuming process. This process is
characterized by a matching function,

h = [e.sup.[kappa]] [v.sup.[alpha]] [u.sup.1-[alpha]], (1)

where h is the number of new hires when v vacancies are matched
with u unemployed workers, and [alpha] [member of] [0, 1] is the
elasticity of new hires with respect to vacancies. The matching function
is constant returns to scale, that is, if the number of vacancies and
unemployed doubles, then the number of new matches also doubles. In
fact, the usual specification of the matching function in equation (1)
is analogous to a Cobb-Douglas production function where unemployed
workers and vacancies are inputs to a process that generates new filled
positions. This process may be more or less efficient, and the matching
efficiency [kappa] reflects the extent of frictions in the labor market.
The smaller the matching efficiency, the less efficient the labor market
is at matching the unemployed with open positions.

The rate at which unemployed workers make the transition to
employment is

where the vacancy-unemployment ratio [theta] denotes "labor
market tightness." (3) Conditional on the matching elasticity, we
can recover the matching efficiency from observations on how long it
takes for an unemployed to become employed, that is, the employment
transition rate and market tightness,

[kappa] = ln [lambda] - [alpha]ln [theta]. (3)

Heterogeneous Search

Now suppose that the unemployed differ in their search
effectiveness, but that after accounting for these differences, they are
all perfect substitutes in the matching function. First assume that
there is a finite number of types, J, and that each type is endowed with
[[rho].sub.j] search units. The total effective search input from all of
theses types is

In other words, assuming that all workers in the search pool are
homogeneous when they are not conflates changes in matching efficiency
with changes in average search effectiveness.

2. HETEROGENEITY OF NONEMPLOYMENT

We now briefly describe the components of nonemployment that we use
in the construction of our nonemployment index. This section is closely
related to Section 1 of Hornstein, Kudlyak, and Lange (2014).

The BLS Classification Scheme

Among the most widely reported statistics from the Bureau of Labor
Statistics (BLS) are the shares of the working-age population who are
currently employed, unemployed, and OLF. These shares are estimated
using responses from the monthly CPS. A nonemployed respondent is
counted as unemployed if she has been actively looking for work in the
month preceding the survey week. Those neither employed nor actively
looking for work are classified as OLF. Starting with the comprehensive
revision of the CPS in 1994, the BLS provides additional detail on the
labor market attachment of the nonemployed based on survey responses as
to why an individual is not actively looking for work (see Polivka and
Miller [1998] for a description of the 1994 CPS revision). The average
population shares for the different nonemployment categories in the CPS
are listed in Table 1, in columns 1a and 1b. We report the average
shares for the years 1994-2007 in column 1a and for the years 2008-16 in
column 1b. The first sample represents a relatively strong labor market:
it includes two expansions, in particular, the late 1990s information
technology boom, and the shallow 2001 recession. The second sample is
dominated by the 2008-09 Great Recession and represents a relatively
weak labor market.

The unemployed can be subdivided based on their reported length of
unemployment. Short-term unemployment (STU) covers those who have been
unemployed for 26 or fewer weeks, while long-term unemployment (LTU)
encompasses those who have been unemployed for more than 26 weeks. Prior
to the Great Recession, on average, less than one-fifth of all
unemployed report more than 26 weeks of unemployment in any one month.
But the unemployed represent only one-tenth of the nonemployed. The
remaining nine-tenths are OLF.

A little less than one-tenth of the OLF declare that they do want
to work, even though they did not actively look for work in the previous
month. Those in this group who want a job, are available for work, and
searched for work within the last year (not the last month) are
classified as marginally attached. On average, about one-fourth of those
who want work are marginally attached, and there are six times as many
unemployed as there are marginally attached respondents. Those
marginally attached who did not search for a job during the last month
because they were discouraged over job prospects are classified as
discouraged. On average, discouraged individuals make up about one-third
of the marginally attached. But over nine-tenths of those OLF do not
want a job. Among these individuals we can distinguish between those who
are retired, disabled, currently in school, and the remainder. On
average, the retired and disabled account for about two-thirds of those
who do not want work.

Despite the recent decline of unemployment to historically low
levels in 2016, in the aftermath of the 2007-09 recession average
non-employment is about 4 percentage points higher than it was prior to
the recession. Comparing columns (1a) and (1b) of Table 1, we see that
the main drivers of this increase of nonemployment were higher LTU,
disability and retirement, and more people in school, whereas the share
of those OLF who want to work remained relatively stable. The share of
LTU increased to close to one-half of total unemployment and has
remained high even though overall unemployment has declined. Some of the
increase in disability may be in response to the weak labor market of
the Great Recession, but it also reflects the continuation of a positive
trend established in prior years. Finally, the increased retirement
share reflects the demographics of an aging U.S. population.

Transitions to Employment

We are motivated to examine broader nonemployment concepts since
the distinction between unemployment and OLF is not as sharp as one
would think. In fact, from month to month, roughly twice as many
individuals transition from OLF to employment as transition from
unemployment. We now show that for all of our nonemployment groups, the
transition probabilities to employment are positive and that the
heterogeneity in these transition probabilities seems to be consistent
with the self-reported labor market attachment.

We first use the CPS microdata to construct exit probabilities from
nonemployment using the short rotating four-month panels in the CPS. In
any month, we observe the labor market status in the current and
following month for roughly three-fourths of the sample. Based on the
responses to the CPS questions, we group the nonemployed into the nine
nonemployment segments discussed above: the two duration segments of the
unemployed, the three segments of OLF who want a job (marginally
attached, discouraged, other), and the four segments of OLF who do not
want a job (retired, disabled, in school, not in school). We then
construct the transition probabilities into employment or a different
nonemployment state for each segment by matching the individual records
from the CPS microdata month to month. (4) The transition probability
from a particular segment of nonemployment is the fraction of that
segment that exits to employment, [p.sub.E], or to a different segment
of nonemployment, [p.sub.NE], from one month to the next.

Table 1, column 2, shows annual averages of the monthly employment
transition probabilities for the two unemployment segments and seven OLF
segments averaged across 1994-2007 and 2008-16. The chances of becoming
employed differ substantially among these groups. The employment
probabilities are highest for the short-term unemployed: on average,
they have a 30 percent chance of finding a job within a month. Next are
the LTU and those OLF individuals who want a job: they are about half as
likely to become employed as are the STU. (5) Then there is the group of
those who do not want a job but who are neither retired nor disabled:
they are only one-fourth as likely to become employed as are the STU.
Finally, there is the group of retired and disabled who are less than
one-tenth as likely to become employed as are the STU. (6)

In recessions the employment probabilities tend to fall for all
groups, but the ranking of the different groups in terms of their
transition probabilities to employment remains the same. (7) This is
also apparent when comparing the pre- and post-Great Recession period,
columns 2a and 2b: even though the average transition probabilities are
uniformly lower in the post-2008 period, the relative transition
probabilities are not that different. Furthermore, the ranking of
employment probabilities coincides with the desire to work as stated in
the survey: those who actively search tend to have higher transition
rates to employment than those who want to work but do not actively look
for work, and those who want to work have higher transition rates than
those who do not want to work.

Table 1, column 3, shows annual averages of the monthly transition
probabilities to a different nonemployment state for the two
unemployment segments and seven OLF segments averaged across 1994-2007
and 2008-16. Again, the chances of making the transition to a different
nonemployment state differ substantially among these groups, and again
the STU stand out. For the STU, the probability of making the transition
to a different nonemployment state is slightly lower than the
probability of becoming employed, whereas the opposite is true for all
other nonemployment states. This is especially noteworthy for those OLF
who want to work but are classified as OLF because they do not state
that they are actively looking for work. For this group, the probability
of exiting to a different nonemployment state is four to five times
higher than the probability of becoming employed. It is quite possible
that these high probabilities of switching to a different nonemployment
state simply mean that individuals in these groups will in the next
month state that they are actively looking for work. That being the
case, the fact that for all groups except the STU the transition
probabilities to some other nonemployment state are higher than the
transition probability to employment suggests that looking at the
employment transition probability alone as a measure of labor market
attachment might be misleading.

We elaborate on the issue of how transition probabilities to
employment and some other nonemployment state jointly reflect the
transitions to employment in the Appendix. When transitions between
employment and nonemployment states take place continuously, the
month-to-month transition probabilities that we calculate from the CPS
between two points in time reflect this underlying process. In
particular, a relatively high transition rate to nonemployment states
may mask the true transitions to employment in the employment transition
probability. Effectively, the employment transition probability from
month to month may appear to be low not because the transition rate to
employment is low, but because the transition rate to other
nonemployment states with low exit rates to employment is high. In Table
2, we report the employment transition rates using either employment
transition probabilities alone in column 1 or transition probabilities
to employment and nonemployment jointly in column 2. (8) Accounting for
the interaction between transitions to employment and other
nonemployment states tends to increase the estimated level of employment
transition rates, but for all nonemployment segments except for the OLF
who want to work it does not affect the employment transition rates
relative to the transition rates of the STU.

Heterogeneous Search Pools

We have motivated the NEI in Hornstein et al. (2014) as a way to
capture persistent differences in labor market attachment across groups
through their average employment transition rates. The same persistent
differences in transitions to employment play an integral part in the
generalized matching function with heterogeneous search efficiencies
described in Section 1. From this perspective, the important
distinctions between the different nonemployment states that enter the
NEI and the generalized matching function are (1) short-term
unemployment and (2) long-term unemployment, (3) those who are OLF and
want to work, (4) those who are OLF, do not want to work, are in school,
and others, and (5) those who are OLF, do not want to work, and are
disabled or retired. For this aggregation of nonemployment states, the
differences of employment transitions across groups clearly dominate the
differences within groups. We now describe how the composition and the
employment transitions of this "aggregated" search pool change
with the business cycle.

In Figure 1, we plot the working-age population shares of the five
aggregated nonemployment segments for the period 1994-2016. From this
graph it is apparent that for the two recessions in the sample period,
2001 and 2007-09, the nonemployment share is increasing mainly because
of increased unemployment. The increase of LTU in the Great Recession is
especially striking. Following the recovery from the Great Recession,
the decline in unemployment was compensated by an increase of those who
are disabled or retired such that the working-age share of nonemployment
remained elevated.

In Figure 2, we plot the employment transition rates of the five
aggregated nonemployment segments for the period 1994-2016. (9) The
figure reflects the persistent differences in employment transition
rates across different nonemployment segments. In particular, employment
transition rates across nonemployment segments move together, they
decline in recessions and increase in recoveries such that the ranking
of transition rates remain unchanged. (10) This does not preclude
different cyclical sensitivities for the transition rates of different
nonemployment segments, but it appears that the volatility of employment
transition rates relative to those of the STU is limited, Figure 3. (11)

In Table 3, we summarize the average properties of working-age
population shares and relative employment transitions for our five
aggregated nonemployment segments. As we have noted, nonemployment has
somewhat increased in the years following the Great Recession, and most
of the increase took place among the LTU and the disabled and retired,
Table 3 column 1. Even though transitions to employment declined
substantially following the Great Recession, the decline affected all
nonemployment segments equally, such that the transitions of all
segments relative to those of the STU remained quite stable. This
stability of relative employment transitions holds independently of how
we measure employment transitions, whether it is the straight employment
transition probability, Table 3 column 2, or the employment transition
rate calculated from the exit probabilities to employment and a
different nonemployment state, Table 3 column 3. In Section 2, we have
argued that the employment transition rate represents a better measure
of employment transitions, and for the following, we will use the
average employment transition rates for the full sample, the average of
Table 3 column 3a and column 3b, as our measure of the relative quality
of the different nonemployment segments. (12)

3. MATCHING EFFICIENCY AND THE NEI

We now use the information on relative employment transition rates
to construct measures of quality-adjusted search input for a matching
function with heterogeneous search efficiencies as described in Section
1, equation (4). These quality-adjusted search input measures correspond
to the nonemployment index we proposed in Hornstein et al. (2014). We
then show that measures of matching efficiency for generalized matching
functions that account for heterogeneity are less volatile than the
matching efficiency measures derived from standard matching functions
that assume homogeneous search and are limited to the unemployment pool.

We proceed by gradually expanding our definition of the search
pool. For the first definition (NEI1), we take the weighted sum of STU
and LTU, where STU receives a weight of 1 and LTU receives a weight of
0.46. The weight of LTU is its average employment transition rate
relative to STU or, using the heterogeneous search framework

since [[rho].sub.STU] [equivalent to] 1. (13) For the second
definition (NEI2), we add the OLF who want to work with a weight of 0.71
to NEI1. Finally, for the third definition (NEI3), we add the OLF who
are at school with a weight of 0.24 and the disabled and retired with a
weight of 0.04 to NEI2. The working-age population shares of the three
quality-adjusted search pools are displayed in Figure 4. For comparison,
we have also added the working-age population share of the unweighted
unemployed (U), which represents the standard measure of unemployment.

By construction, the level of the NEIs is increasing as we expand
the coverage of nonemployment. In particular, once we include weighted
OLF (NEI2 and NEI3), the levels of the NEIs are larger than for the
standard measure of unemployment U. But note that the NEIs tend to be
less volatile than the standard measure of unemployment, that is, they
increase less in recessions than does the standard measure of
unemployment. Furthermore, like the unemployment rate, all NEIs have
essentially returned to their pre-Great Recession lows.

The proposed NEIs represent the quality-adjusted input to a
generalized matching function that accounts for heterogeneity in search
efficiencies across types. Following the discussion in Section 1, we can
decompose changes in the average employment transition rate across all
nonemployment segments included in an NEI, [bar.[lambda]], into changes
coming from market tightness, [theta], average search pool quality,
[bar.[rho]], and aggregate matching efficiency, [kappa], equation (7).
We construct market tightness, that is, the ratio of vacancies to the
unweighted sum of nonemployment segments in the NEI, using the adjusted
help-wanted index (HWI) from Barnichon (2010) for vacancies and posted
job openings from JOLTS. (14) In Figure 5, we plot the average
employment transition rates (A), market tightness (B), average quality
(C), and matching efficiency (D) for our three NEI definitions. (15) For
comparison, we also plot average quality and matching efficiency for the
standard measure of unweighted unemployment.

The average employment transition rate declines in recessions and
increases in expansions, Figure 5.A. This property of the average
transition rate simply reflects the same countercyclical pattern for all
of the component transition rates. As we expand the coverage of the
search pool, the average transition rate becomes less volatile. (16) In
particular, the average transition rate declines less in recessions.
This is because relative to the employment transition rates of the
unemployed, the transition rates of the OLF (want work) decline less in
recessions (NEI2 versus NEI1), as do the transition rates of the OLF (do
not want work) (NEI3 versus NEI2). Furthermore, the unemployed with
highly volatile transition rates represent a relatively small share of
NEI3.

Market tightness has the same cyclical pattern as the average
employment transition rate: it declines in recessions and increases in
expansions, Figure 5.B. This feature reflects the fact that in
recessions vacancy postings decline and nonemployment increases. The
volatility of market tightness also declines as we expand the coverage
of the search pool, and this reflects the fact that unweighted, like
weighted, (NEI) nonemployment becomes less volatile as we expand the
coverage of the search pool, Figure 4.

In the standard matching framework with homogeneous search, average
quality is constant. In the generalized matching framework with
heterogeneous search, average quality reflects the composition of the
search pool, Figure 5.C. For example, average quality for
quality-adjusted unemployment (NEI1) declines in recessions because the
share of LTU with relatively low search efficiency is increasing in
recessions. Average quality continues to decline in recessions for the
search pool (NEI2) that includes OLF (want work), but the magnitude of
the decline is reduced since the weight of OLF (want work) is more
similar to STU than it is to LTU. For the broadest definition of the
search pool (NEI3) that includes OLF (do not want work), average quality
increases in recessions. This is unlike what we see for the two narrower
definitions of the search pool and occurs because the share of OLF (do
not want work) in total nonemployment declines in recessions and both
components of OLF (do not want work) receive smaller quality weights
than all other nonemployment components in the search pool.

Finally, matching efficiency represents the residual component
that, together with market tightness and average quality, accounts for
the movements in average employment transition rates. In Figure 5.D, we
use equation (7) to construct measures of matching efficiency for the
different search pool definitions. We assume that the matching
elasticity is [alpha] = 0.35, a value consistent with estimates from
Barnichon and Figura (2015) and within the range of reported matching
elasticities from Petrongolo and Pissarides (2001). We start with the
matching efficiency calculated for the standard search pool definition
with homogeneous unemployment (U). For this measure, the decline in
matching-elasticity weighted market tightness accounts for some of the
decline in average transition rates, but with no change in average
quality a significant decline in matching efficiency remains. Once we
account for heterogeneity in the search pool of unemployed (NEI1),
average quality declines in recessions and less of a decline in matching
efficiency is required. Once we include OLF (want work) in the search
pool (NEI2), the average transition rate and market tightness both
decline less in recessions, but the change is more pronounced for the
average transition rate such that a smaller decline of matching
efficiency is required. Finally, for the most comprehensive definition
of the search pool (NEI3), which includes OLF (do not want work),
average employment transition rates are even less volatile relative to
market tightness and average quality increases in recessions such that
substantially smaller declines in matching efficiency occur during
recessions.

4. CONCLUSION

We have reviewed the evidence on heterogeneity among the
nonemployed in the CPS with respect to their likelihood of making the
transition to employment within a month, and we have shown that while
the differences between the groups that are most and least likely to
make the transition to employment are quantitatively substantial, there
is also a gradual transition between the groups at the extremes. We have
then shown that the NEI proposed in Hornstein et al. (2014) represents
the quality-adjusted search input of a generalized matching function
that accounts for heterogeneity in search efficiency across the search
pool. Finally, expanding the coverage of the search pool at the same
time one accounts for heterogeneity in search effort reduces the
measured decline in matching efficiency associated with the Great
Recession. In other words, for an appropriately defined broader concept
of nonemployment, the efficiency of the U.S. labor market has not
declined as much as would be suggested by standard measures of
unemployment.

APPENDIX

Data for the population shares and employment transition rates for
nonemployment by reason are constructed from the monthly CPS micro
datasets as in Kudlyak and Lange (2014). All data are seasonally
adjusted using the procedure proposed by Watson (1996). We deviate from
Hornstein et al. (2014) in the construction of the employment transition
rates in order to account for the possibility that the nonemployment
state may change not only because a nonemployed worker makes the
transition to employment, but also because she may just make the
transition to a different nonemployment state. Both transition rates
will be reflected in the transition probability to employment, but from
a matching function perspective we are mainly interested in the
transition rate to employment.

Take a group with nonemployment status j. Assume that transitions
to employment or a different nonemployment state arrive continuously
according to Poisson processes with arrival rates [[lambda].sub.jE] and
[[lambda].sub.jN], respectively. Then the probability that within a
month a member will exit nonemployment state j for employment is

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII],

ignoring the possibility that somebody will flow back into state j
in the same month. (17) We can simplify this expression and apply the
same procedure to the exit probability to a different nonemployment
state, and we get

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

We have data on the monthly transition probabilities to employment,
[p.sub.jE], or a different nonemployment state, [p.sub.jN]. We can
recover the transition rates [lambda] from the transition probabilities
p as follows

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII].

For [p.sub.jN] small relative to [p.sub.jE] we have

[[lambda].sub.jE] [approximately equal to] log (1 - [p.sub.jE]),

that is, we can limit attention to the employment transition
probabilities. Note that the exit rates are defined on the unit
interval, which represents one month. So we are calculating monthly exit
rates.

REFERENCES

Appelbaum, Binyamin. 2014. "Still Needed: Millions of
Jobs." New York Times, April 4.

Polivka, Anne E., and Stephen M. Miller. 1998. "The CPS After
the Redesign: Refocusing the Economic Lens." In Labor Statistics
Measurement Issues; Studies in Income and Wealth, vol. 60, edited by
John Haltiwanger, Marilyn E. Manser, and Robert Topel. Chicago:
University of Chicago Press, 249-89.

Andreas Hornstein is a senior advisor at the Federal Reserve Bank
of Richmond and Marianna Kudlyak is a senior economist in the Research
Department at the Federal Reserve Bank of San Francisco. The authors
thank Sean McCrary for excellent research assistance and Marios
Karabarbounis, Santiago Pinto, Allen Sirolly, and John Weinberg for
helpful comments. The views expressed in this article are those of the
authors and not necessarily those of the Federal Reserve Bank of
Richmond, the Federal Reserve Bank of San Francisco, or the Federal
Reserve System. E-mail: Andreas.Hornstein@rich.frb.org;
Marianna.Kudlyak@sf.frb.org.

(2) For example, Pissarides (2000) or Petrongolo and Pissarides
(2001).

(3) We interpret the transitions as occurring continuously over
time. In particular, we assume that employment opportunities arrive
according to a Poisson process with arrival rate [lambda]. In this case,
a worker who is unemployed at the beginning of the period will be
employed at the end of the period with probability 1 -
[e.sup.-[lambda]]. See also the Appendix.

(4) Our matching procedure follows the algorithms described in
Madrian and Lefgren (1999) and Shimer (2012) The CPS microdata fields
are available at http://thedataweb.rm.census.gov/ftp/cps_ftp.html#cpsbasic.

(5) Note that the employment transition probabilities among the
marginally attached OLF do not differ much. In particular, there is no
reason to single out discouraged workers based on the likelihood of
becoming employed again.

(6) See also Fujita (2014).

(7) See Kudlyak and Lange (2014) for graphs of annual averages of
monthly job finding rates for the years 1994 to 2013. See also Figures 2
and 3.

(8) In the Appendix, we describe how the transition probabilities
can be used to recover the transition rates that generate the observed
transition probabilities.

(9) The "aggregated" employment transition rates are
calculated as the nonemployed weighted averages of the employment
transition rates calculated using data on exit probabilities to
employment and other nonemployment states.

(10) There also appears to be a secular decline in employment
transition rates for unemployed and those OLF who want to work.

(11) Hornstein and Kudlyak (2016) use these different cyclical
sensitivities to identify differences in search effort across segments.

(12) Using average relative transition rates from the pre-2008
period does not change the results.

(13) Assigning a weight of one to STU is a normalization. Choosing
a different weight for STU while maintaining the relative weights
between the different groups affects the scale of the NEI but not its
cyclical properties.

(14) The HWI index is available from the 1970s on, whereas JOLTS
data are available only from 2000 on. The shift in job advertising from
print media to web-based means that the HWI may not be consistent over
time. Barnichon (2010) corrects for these structural changes in the HWI
series in a way such that the HWI lines up with the JOLTS job openings
in mid-2000, and we splice the two series in 2006.

(15) We plot the log of each series and normalize each series to
zero at the beginning of the sample.

(16) The level of the average employment transition rate also
declines as we expand the coverage of the search pool, but this is not
apparent from Figure 5.A since we have normalized each series to zero at
the beginning of the sample.

(17) Shimer (2012) proposes a procedure that recovers continuous
time exit rates allowing for the possibility that an agent who exits a
state returns to the state within the unit of observation. His procedure
uses information from the complete transition matrix covering
transitions between all labor market states.